Artificial intelligence: a critical review of current applications in pancreatic imaging


The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreatic tumors but also for pancreatic lesion characterization. In this review, the basic of radiomics, recent developments and current results of AI in the field of pancreatic tumors are presented. Limitations and future perspectives of AI are discussed.

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Fig. 1
Fig. 2



Three dimensional


Artificial intelligence


Autoimmune pancreatitis


Artificial neural network


Area under receiver operating characteristic curve


Computer-aided diagnosis


Convolutional neural networks


Confidence interval


Computed tomography


Discriminative dictionary learning for segmentation


Deep learning


Dice similarity coefficient


Intraductal papillary mucinous neoplasm


Mucinous cystic neoplasms


Machine learning


Magnetic resonance imaging


Pancreatic ductal adenocarcinoma


Random forests


Serous cystic neoplasms


Solid pseudopapillary epithelial neoplasms


Support vector machines


Volume of interest


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Correspondence to Philippe Soyer.

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Barat, M., Chassagnon, G., Dohan, A. et al. Artificial intelligence: a critical review of current applications in pancreatic imaging. Jpn J Radiol (2021).

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  • Artificial intelligence
  • Pancreatic neoplasms
  • Radiomics
  • Texture analysis